diff --git a/src/sagemaker/automl/automl.py b/src/sagemaker/automl/automl.py
index 247a5ae71e..ed9805f75e 100644
--- a/src/sagemaker/automl/automl.py
+++ b/src/sagemaker/automl/automl.py
@@ -216,8 +216,7 @@ def deploy(
                 created from it.
             sagemaker_session (sagemaker.session.Session): A SageMaker Session
                 object, used for SageMaker interactions (default: None). If not
-                specified, one is created using the default AWS configuration
-                chain.
+                specified, the one originally associated with the ``AutoML`` instance is used.
             name (str): The pipeline model name. If None, a default model name will
                 be selected on each ``deploy``.
             endpoint_name (str): The name of the endpoint to create (default:
@@ -248,6 +247,8 @@ def deploy(
                 If ``predictor_cls`` is specified, the invocation of ``self.predictor_cls`` on
                 the created endpoint name. Otherwise, ``None``.
         """
+        sagemaker_session = sagemaker_session or self.sagemaker_session
+
         if candidate is None:
             candidate_dict = self.best_candidate()
             candidate = CandidateEstimator(candidate_dict, sagemaker_session=sagemaker_session)
diff --git a/tests/unit/sagemaker/automl/test_auto_ml.py b/tests/unit/sagemaker/automl/test_auto_ml.py
index d4fd5fe236..c6b240e2b2 100644
--- a/tests/unit/sagemaker/automl/test_auto_ml.py
+++ b/tests/unit/sagemaker/automl/test_auto_ml.py
@@ -477,16 +477,19 @@ def test_deploy(sagemaker_session, candidate_mock):
     )
 
 
-def test_deploy_optional_args(sagemaker_session, candidate_mock):
+@patch("sagemaker.automl.automl.CandidateEstimator")
+def test_deploy_optional_args(candidate_estimator, sagemaker_session, candidate_mock):
+    candidate_estimator.return_value = candidate_mock
+
     auto_ml = AutoML(
         role=ROLE, target_attribute_name=TARGET_ATTRIBUTE_NAME, sagemaker_session=sagemaker_session
     )
-    auto_ml.best_candidate = Mock(name="best_candidate", return_value=CANDIDATE_DICT)
     auto_ml._deploy_inference_pipeline = Mock("_deploy_inference_pipeline", return_value=None)
 
     auto_ml.deploy(
         initial_instance_count=INSTANCE_COUNT,
         instance_type=INSTANCE_TYPE,
+        candidate=CANDIDATE_DICT,
         sagemaker_session=sagemaker_session,
         name=JOB_NAME,
         endpoint_name=JOB_NAME,
@@ -515,6 +518,8 @@ def test_deploy_optional_args(sagemaker_session, candidate_mock):
         predictor_cls=RealTimePredictor,
     )
 
+    candidate_estimator.assert_called_with(CANDIDATE_DICT, sagemaker_session=sagemaker_session)
+
 
 def test_candidate_estimator_get_steps(sagemaker_session):
     candidate_estimator = CandidateEstimator(CANDIDATE_DICT, sagemaker_session=sagemaker_session)